Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations97690
Missing cells266
Missing cells (%)< 0.1%
Duplicate rows13979
Duplicate rows (%)14.3%
Total size in memory9.7 MiB
Average record size in memory104.0 B

Variable types

Numeric5
Categorical3
Text5

Alerts

Dataset has 13979 (14.3%) duplicate rowsDuplicates
cafv_eligibility is highly overall correlated with electric_range and 2 other fieldsHigh correlation
electric_range is highly overall correlated with cafv_eligibility and 2 other fieldsHigh correlation
ev_type is highly overall correlated with cafv_eligibility and 3 other fieldsHigh correlation
make is highly overall correlated with cafv_eligibility and 2 other fieldsHigh correlation
model_year is highly overall correlated with ev_typeHigh correlation
postal_code is highly skewed (γ1 = -26.34344976) Skewed
census_tract is highly skewed (γ1 = -24.83581899) Skewed

Reproduction

Analysis started2025-06-20 10:56:40.394529
Analysis finished2025-06-20 10:58:21.011393
Duration1 minute and 40.62 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

model_year
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.1977
Minimum2000
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size763.3 KiB
2025-06-20T16:28:21.287735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2013
Q12017
median2019
Q32022
95-th percentile2025
Maximum2025
Range25
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4100811
Coefficient of variation (CV)0.0016888297
Kurtosis-0.56216769
Mean2019.1977
Median Absolute Deviation (MAD)2
Skewness-0.1338528
Sum1.9725542 × 108
Variance11.628653
MonotonicityNot monotonic
2025-06-20T16:28:21.714653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2018 14504
14.8%
2020 12315
12.6%
2019 11051
11.3%
2024 10084
10.3%
2017 8755
9.0%
2023 6798
7.0%
2016 5302
 
5.4%
2025 4952
 
5.1%
2021 4906
 
5.0%
2022 4710
 
4.8%
Other values (10) 14313
14.7%
ValueCountFrequency (%)
2000 8
 
< 0.1%
2002 2
 
< 0.1%
2003 1
 
< 0.1%
2008 16
 
< 0.1%
2010 23
 
< 0.1%
2011 656
 
0.7%
2012 1462
 
1.5%
2013 4168
4.3%
2014 3364
3.4%
2015 4613
4.7%
ValueCountFrequency (%)
2025 4952
 
5.1%
2024 10084
10.3%
2023 6798
7.0%
2022 4710
 
4.8%
2021 4906
 
5.0%
2020 12315
12.6%
2019 11051
11.3%
2018 14504
14.8%
2017 8755
9.0%
2016 5302
 
5.4%

make
Categorical

High correlation 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size763.3 KiB
TESLA
25347 
NISSAN
10221 
CHEVROLET
10173 
TOYOTA
8741 
BMW
6611 
Other values (31)
36597 

Length

Max length20
Median length14
Mean length5.6072577
Min length3

Characters and Unicode

Total characters547773
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNISSAN
2nd rowKIA
3rd rowTESLA
4th rowJEEP
5th rowTESLA

Common Values

ValueCountFrequency (%)
TESLA 25347
25.9%
NISSAN 10221
10.5%
CHEVROLET 10173
10.4%
TOYOTA 8741
 
8.9%
BMW 6611
 
6.8%
JEEP 6086
 
6.2%
KIA 5059
 
5.2%
VOLVO 4237
 
4.3%
FORD 3999
 
4.1%
CHRYSLER 3479
 
3.6%
Other values (26) 13737
14.1%

Length

2025-06-20T16:28:22.199864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 25347
25.9%
nissan 10221
10.4%
chevrolet 10173
10.4%
toyota 8741
 
8.9%
bmw 6611
 
6.7%
jeep 6086
 
6.2%
kia 5059
 
5.2%
volvo 4237
 
4.3%
ford 3999
 
4.1%
chrysler 3479
 
3.6%
Other values (31) 14014
14.3%

Most occurring characters

ValueCountFrequency (%)
E 68363
12.5%
A 60075
11.0%
S 55569
10.1%
T 55316
10.1%
L 46422
 
8.5%
O 44204
 
8.1%
N 26151
 
4.8%
I 24755
 
4.5%
R 24151
 
4.4%
V 19879
 
3.6%
Other values (18) 122888
22.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 546581
99.8%
Dash Punctuation 910
 
0.2%
Space Separator 277
 
0.1%
Other Punctuation 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 68363
12.5%
A 60075
11.0%
S 55569
10.2%
T 55316
10.1%
L 46422
 
8.5%
O 44204
 
8.1%
N 26151
 
4.8%
I 24755
 
4.5%
R 24151
 
4.4%
V 19879
 
3.6%
Other values (15) 121696
22.3%
Dash Punctuation
ValueCountFrequency (%)
- 910
100.0%
Space Separator
ValueCountFrequency (%)
277
100.0%
Other Punctuation
ValueCountFrequency (%)
! 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 546581
99.8%
Common 1192
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 68363
12.5%
A 60075
11.0%
S 55569
10.2%
T 55316
10.1%
L 46422
 
8.5%
O 44204
 
8.1%
N 26151
 
4.8%
I 24755
 
4.5%
R 24151
 
4.4%
V 19879
 
3.6%
Other values (15) 121696
22.3%
Common
ValueCountFrequency (%)
- 910
76.3%
277
 
23.2%
! 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 547773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 68363
12.5%
A 60075
11.0%
S 55569
10.1%
T 55316
10.1%
L 46422
 
8.5%
O 44204
 
8.1%
N 26151
 
4.8%
I 24755
 
4.5%
R 24151
 
4.4%
V 19879
 
3.6%
Other values (18) 122888
22.4%

model
Text

Distinct105
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size763.3 KiB
2025-06-20T16:28:22.983666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length6.8238612
Min length2

Characters and Unicode

Total characters666623
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowLEAF
2nd rowSOUL
3rd rowMODEL 3
4th rowWRANGLER
5th rowMODEL 3
ValueCountFrequency (%)
model 25303
17.2%
3 13829
 
9.4%
leaf 10221
 
6.9%
prime 7723
 
5.2%
phev 7723
 
5.2%
s 5797
 
3.9%
bolt 5261
 
3.6%
ev 5261
 
3.6%
volt 4686
 
3.2%
prius 4563
 
3.1%
Other values (104) 57087
38.7%
2025-06-20T16:28:24.141578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 77360
 
11.6%
L 55535
 
8.3%
O 50782
 
7.6%
49764
 
7.5%
R 38957
 
5.8%
A 38641
 
5.8%
M 35145
 
5.3%
D 28213
 
4.2%
I 27570
 
4.1%
P 26650
 
4.0%
Other values (28) 238006
35.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 553951
83.1%
Space Separator 49764
 
7.5%
Decimal Number 41913
 
6.3%
Close Punctuation 7723
 
1.2%
Open Punctuation 7723
 
1.2%
Dash Punctuation 5549
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 77360
14.0%
L 55535
 
10.0%
O 50782
 
9.2%
R 38957
 
7.0%
A 38641
 
7.0%
M 35145
 
6.3%
D 28213
 
5.1%
I 27570
 
5.0%
P 26650
 
4.8%
V 22495
 
4.1%
Other values (14) 152603
27.5%
Decimal Number
ValueCountFrequency (%)
3 18105
43.2%
0 7936
18.9%
5 5853
 
14.0%
4 4225
 
10.1%
9 3233
 
7.7%
6 2072
 
4.9%
2 207
 
0.5%
7 176
 
0.4%
8 105
 
0.3%
1 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
49764
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7723
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7723
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5549
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 553951
83.1%
Common 112672
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 77360
14.0%
L 55535
 
10.0%
O 50782
 
9.2%
R 38957
 
7.0%
A 38641
 
7.0%
M 35145
 
6.3%
D 28213
 
5.1%
I 27570
 
5.0%
P 26650
 
4.8%
V 22495
 
4.1%
Other values (14) 152603
27.5%
Common
ValueCountFrequency (%)
49764
44.2%
3 18105
 
16.1%
0 7936
 
7.0%
) 7723
 
6.9%
( 7723
 
6.9%
5 5853
 
5.2%
- 5549
 
4.9%
4 4225
 
3.7%
9 3233
 
2.9%
6 2072
 
1.8%
Other values (4) 489
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 666623
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 77360
 
11.6%
L 55535
 
8.3%
O 50782
 
7.6%
49764
 
7.5%
R 38957
 
5.8%
A 38641
 
5.8%
M 35145
 
5.3%
D 28213
 
4.2%
I 27570
 
4.1%
P 26650
 
4.0%
Other values (28) 238006
35.7%

ev_type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size763.3 KiB
Plug-in Hybrid Electric Vehicle (PHEV)
50178 
Battery Electric Vehicle (BEV)
47512 

Length

Max length38
Median length38
Mean length34.109162
Min length30

Characters and Unicode

Total characters3332124
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBattery Electric Vehicle (BEV)
2nd rowBattery Electric Vehicle (BEV)
3rd rowBattery Electric Vehicle (BEV)
4th rowPlug-in Hybrid Electric Vehicle (PHEV)
5th rowBattery Electric Vehicle (BEV)

Common Values

ValueCountFrequency (%)
Plug-in Hybrid Electric Vehicle (PHEV) 50178
51.4%
Battery Electric Vehicle (BEV) 47512
48.6%

Length

2025-06-20T16:28:24.645500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-20T16:28:25.046154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
electric 97690
22.2%
vehicle 97690
22.2%
plug-in 50178
11.4%
hybrid 50178
11.4%
phev 50178
11.4%
battery 47512
10.8%
bev 47512
10.8%

Most occurring characters

ValueCountFrequency (%)
343248
 
10.3%
e 340582
 
10.2%
i 295736
 
8.9%
c 293070
 
8.8%
l 245558
 
7.4%
r 195380
 
5.9%
V 195380
 
5.9%
E 195380
 
5.9%
t 192714
 
5.8%
P 100356
 
3.0%
Other values (13) 934720
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2056822
61.7%
Uppercase Letter 686496
 
20.6%
Space Separator 343248
 
10.3%
Open Punctuation 97690
 
2.9%
Close Punctuation 97690
 
2.9%
Dash Punctuation 50178
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 340582
16.6%
i 295736
14.4%
c 293070
14.2%
l 245558
11.9%
r 195380
9.5%
t 192714
9.4%
y 97690
 
4.7%
h 97690
 
4.7%
b 50178
 
2.4%
d 50178
 
2.4%
Other values (4) 198046
9.6%
Uppercase Letter
ValueCountFrequency (%)
V 195380
28.5%
E 195380
28.5%
P 100356
14.6%
H 100356
14.6%
B 95024
13.8%
Space Separator
ValueCountFrequency (%)
343248
100.0%
Open Punctuation
ValueCountFrequency (%)
( 97690
100.0%
Close Punctuation
ValueCountFrequency (%)
) 97690
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2743318
82.3%
Common 588806
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 340582
12.4%
i 295736
10.8%
c 293070
10.7%
l 245558
9.0%
r 195380
 
7.1%
V 195380
 
7.1%
E 195380
 
7.1%
t 192714
 
7.0%
P 100356
 
3.7%
H 100356
 
3.7%
Other values (9) 588806
21.5%
Common
ValueCountFrequency (%)
343248
58.3%
( 97690
 
16.6%
) 97690
 
16.6%
- 50178
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3332124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
343248
 
10.3%
e 340582
 
10.2%
i 295736
 
8.9%
c 293070
 
8.8%
l 245558
 
7.4%
r 195380
 
5.9%
V 195380
 
5.9%
E 195380
 
5.9%
t 192714
 
5.8%
P 100356
 
3.0%
Other values (13) 934720
28.1%

electric_range
Real number (ℝ)

High correlation 

Distinct110
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.03043
Minimum1
Maximum337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size763.3 KiB
2025-06-20T16:28:25.464682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q130
median58
Q3215
95-th percentile291
Maximum337
Range336
Interquartile range (IQR)185

Descriptive statistics

Standard deviation98.43034
Coefficient of variation (CV)0.87083042
Kurtosis-1.2045914
Mean113.03043
Median Absolute Deviation (MAD)37
Skewness0.61579019
Sum11041943
Variance9688.5318
MonotonicityNot monotonic
2025-06-20T16:28:25.976871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
215 6466
 
6.6%
25 4763
 
4.9%
21 4682
 
4.8%
32 4619
 
4.7%
238 4512
 
4.6%
42 4121
 
4.2%
220 4040
 
4.1%
84 3758
 
3.8%
38 2573
 
2.6%
53 2440
 
2.5%
Other values (100) 55716
57.0%
ValueCountFrequency (%)
1 15
 
< 0.1%
6 967
1.0%
8 33
 
< 0.1%
9 18
 
< 0.1%
10 168
 
0.2%
11 4
 
< 0.1%
12 172
 
0.2%
13 368
 
0.4%
14 1092
1.1%
15 93
 
0.1%
ValueCountFrequency (%)
337 79
 
0.1%
330 332
 
0.3%
322 1733
1.8%
308 555
 
0.6%
293 471
 
0.5%
291 2394
2.5%
289 650
 
0.7%
288 17
 
< 0.1%
270 276
 
0.3%
266 1401
1.4%

cafv_eligibility
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size763.3 KiB
Clean Alternative Fuel Vehicle Eligible
74503 
Not eligible due to low battery range
23187 

Length

Max length39
Median length39
Mean length38.525294
Min length37

Characters and Unicode

Total characters3763536
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClean Alternative Fuel Vehicle Eligible
2nd rowClean Alternative Fuel Vehicle Eligible
3rd rowClean Alternative Fuel Vehicle Eligible
4th rowNot eligible due to low battery range
5th rowClean Alternative Fuel Vehicle Eligible

Common Values

ValueCountFrequency (%)
Clean Alternative Fuel Vehicle Eligible 74503
76.3%
Not eligible due to low battery range 23187
 
23.7%

Length

2025-06-20T16:28:26.517240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-20T16:28:26.885542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
eligible 97690
18.3%
clean 74503
13.9%
alternative 74503
13.9%
fuel 74503
13.9%
vehicle 74503
13.9%
not 23187
 
4.3%
due 23187
 
4.3%
to 23187
 
4.3%
low 23187
 
4.3%
battery 23187
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e 637456
16.9%
l 516579
13.7%
437134
11.6%
i 344386
9.2%
t 241754
 
6.4%
a 195380
 
5.2%
n 172193
 
4.6%
b 120877
 
3.2%
r 120877
 
3.2%
g 120877
 
3.2%
Other values (14) 856023
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2930700
77.9%
Space Separator 437134
 
11.6%
Uppercase Letter 395702
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 637456
21.8%
l 516579
17.6%
i 344386
11.8%
t 241754
 
8.2%
a 195380
 
6.7%
n 172193
 
5.9%
b 120877
 
4.1%
r 120877
 
4.1%
g 120877
 
4.1%
u 97690
 
3.3%
Other values (7) 362631
12.4%
Uppercase Letter
ValueCountFrequency (%)
E 74503
18.8%
C 74503
18.8%
V 74503
18.8%
F 74503
18.8%
A 74503
18.8%
N 23187
 
5.9%
Space Separator
ValueCountFrequency (%)
437134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3326402
88.4%
Common 437134
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 637456
19.2%
l 516579
15.5%
i 344386
10.4%
t 241754
 
7.3%
a 195380
 
5.9%
n 172193
 
5.2%
b 120877
 
3.6%
r 120877
 
3.6%
g 120877
 
3.6%
u 97690
 
2.9%
Other values (13) 758333
22.8%
Common
ValueCountFrequency (%)
437134
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3763536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 637456
16.9%
l 516579
13.7%
437134
11.6%
i 344386
9.2%
t 241754
 
6.4%
a 195380
 
5.2%
n 172193
 
4.6%
b 120877
 
3.2%
r 120877
 
3.2%
g 120877
 
3.2%
Other values (14) 856023
22.7%

county
Text

Distinct157
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size763.3 KiB
2025-06-20T16:28:27.325167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length5.5899887
Min length0

Characters and Unicode

Total characters546086
Distinct characters49
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.1%

Sample

1st rowKing
2nd rowKing
3rd rowKing
4th rowKitsap
5th rowThurston
ValueCountFrequency (%)
king 45475
45.8%
snohomish 10348
 
10.4%
pierce 8152
 
8.2%
clark 6570
 
6.6%
thurston 3977
 
4.0%
kitsap 3718
 
3.7%
spokane 3145
 
3.2%
whatcom 2937
 
3.0%
benton 1600
 
1.6%
skagit 1277
 
1.3%
Other values (160) 12149
 
12.2%
2025-06-20T16:28:28.335814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 73558
13.5%
n 73233
13.4%
K 49771
 
9.1%
g 47220
 
8.6%
o 35327
 
6.5%
a 30165
 
5.5%
h 28561
 
5.2%
e 24312
 
4.5%
s 22578
 
4.1%
r 21890
 
4.0%
Other values (39) 139471
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 445072
81.5%
Uppercase Letter 99347
 
18.2%
Space Separator 1661
 
0.3%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 73558
16.5%
n 73233
16.5%
g 47220
10.6%
o 35327
7.9%
a 30165
6.8%
h 28561
 
6.4%
e 24312
 
5.5%
s 22578
 
5.1%
r 21890
 
4.9%
t 16523
 
3.7%
Other values (14) 71705
16.1%
Uppercase Letter
ValueCountFrequency (%)
K 49771
50.1%
S 15802
 
15.9%
C 8591
 
8.6%
P 8386
 
8.4%
T 3984
 
4.0%
W 3950
 
4.0%
B 1611
 
1.6%
J 1420
 
1.4%
I 1276
 
1.3%
G 900
 
0.9%
Other values (12) 3656
 
3.7%
Other Punctuation
ValueCountFrequency (%)
' 4
66.7%
. 2
33.3%
Space Separator
ValueCountFrequency (%)
1661
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 544419
99.7%
Common 1667
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 73558
13.5%
n 73233
13.5%
K 49771
 
9.1%
g 47220
 
8.7%
o 35327
 
6.5%
a 30165
 
5.5%
h 28561
 
5.2%
e 24312
 
4.5%
s 22578
 
4.1%
r 21890
 
4.0%
Other values (36) 137804
25.3%
Common
ValueCountFrequency (%)
1661
99.6%
' 4
 
0.2%
. 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 546086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 73558
13.5%
n 73233
13.4%
K 49771
 
9.1%
g 47220
 
8.6%
o 35327
 
6.5%
a 30165
 
5.5%
h 28561
 
5.2%
e 24312
 
4.5%
s 22578
 
4.1%
r 21890
 
4.0%
Other values (39) 139471
25.5%

city
Text

Distinct630
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size763.3 KiB
2025-06-20T16:28:29.082592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length8.2769782
Min length0

Characters and Unicode

Total characters808578
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique189 ?
Unique (%)0.2%

Sample

1st rowSeattle
2nd rowRenton
3rd rowSeattle
4th rowBremerton
5th rowOlympia
ValueCountFrequency (%)
seattle 16293
 
14.2%
vancouver 4130
 
3.6%
bellevue 3876
 
3.4%
island 2886
 
2.5%
renton 2711
 
2.4%
redmond 2670
 
2.3%
olympia 2661
 
2.3%
kirkland 2537
 
2.2%
spokane 2379
 
2.1%
tacoma 2369
 
2.1%
Other values (672) 71849
62.8%
2025-06-20T16:28:30.311543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 108655
13.4%
a 80641
 
10.0%
l 68699
 
8.5%
t 56385
 
7.0%
n 55318
 
6.8%
o 48572
 
6.0%
r 35860
 
4.4%
i 31765
 
3.9%
S 27659
 
3.4%
d 25543
 
3.2%
Other values (43) 269481
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 677254
83.8%
Uppercase Letter 114507
 
14.2%
Space Separator 16674
 
2.1%
Dash Punctuation 143
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 108655
16.0%
a 80641
11.9%
l 68699
10.1%
t 56385
 
8.3%
n 55318
 
8.2%
o 48572
 
7.2%
r 35860
 
5.3%
i 31765
 
4.7%
d 25543
 
3.8%
m 24624
 
3.6%
Other values (16) 141192
20.8%
Uppercase Letter
ValueCountFrequency (%)
S 27659
24.2%
B 13401
11.7%
R 7209
 
6.3%
V 6258
 
5.5%
L 5918
 
5.2%
M 5540
 
4.8%
K 5461
 
4.8%
P 5187
 
4.5%
T 4339
 
3.8%
O 4222
 
3.7%
Other values (15) 29313
25.6%
Space Separator
ValueCountFrequency (%)
16674
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 143
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 791761
97.9%
Common 16817
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 108655
13.7%
a 80641
 
10.2%
l 68699
 
8.7%
t 56385
 
7.1%
n 55318
 
7.0%
o 48572
 
6.1%
r 35860
 
4.5%
i 31765
 
4.0%
S 27659
 
3.5%
d 25543
 
3.2%
Other values (41) 252664
31.9%
Common
ValueCountFrequency (%)
16674
99.1%
- 143
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 808578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 108655
13.4%
a 80641
 
10.0%
l 68699
 
8.5%
t 56385
 
7.0%
n 55318
 
6.8%
o 48572
 
6.0%
r 35860
 
4.4%
i 31765
 
3.9%
S 27659
 
3.4%
d 25543
 
3.2%
Other values (43) 269481
33.3%

postal_code
Real number (ℝ)

Skewed 

Distinct763
Distinct (%)0.8%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean98183.658
Minimum2110
Maximum99403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size763.3 KiB
2025-06-20T16:28:30.800077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2110
5-th percentile98008
Q198058
median98166
Q398407.5
95-th percentile99163
Maximum99403
Range97293
Interquartile range (IQR)349.5

Descriptive statistics

Standard deviation2837.5834
Coefficient of variation (CV)0.028900771
Kurtosis728.85168
Mean98183.658
Median Absolute Deviation (MAD)133
Skewness-26.34345
Sum9.591267 × 109
Variance8051879.4
MonotonicityNot monotonic
2025-06-20T16:28:31.288122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 1804
 
1.8%
98115 1527
 
1.6%
98033 1438
 
1.5%
98103 1268
 
1.3%
98012 1251
 
1.3%
98006 1234
 
1.3%
98072 1225
 
1.3%
98040 1189
 
1.2%
98004 1138
 
1.2%
98034 1114
 
1.1%
Other values (753) 84499
86.5%
ValueCountFrequency (%)
2110 1
< 0.1%
2136 1
< 0.1%
2842 2
< 0.1%
2909 1
< 0.1%
3804 1
< 0.1%
4412 1
< 0.1%
6111 1
< 0.1%
6335 1
< 0.1%
6355 1
< 0.1%
6360 1
< 0.1%
ValueCountFrequency (%)
99403 40
 
< 0.1%
99402 8
 
< 0.1%
99371 1
 
< 0.1%
99362 292
0.3%
99361 6
 
< 0.1%
99360 6
 
< 0.1%
99357 10
 
< 0.1%
99354 210
0.2%
99353 172
 
0.2%
99352 440
0.5%
Distinct763
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size763.3 KiB
2025-06-20T16:28:32.087665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length27
Median length27
Mean length26.736053
Min length0

Characters and Unicode

Total characters2611845
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique257 ?
Unique (%)0.3%

Sample

1st rowPOINT (-122.30253 47.72656)
2nd rowPOINT (-122.08747 47.4466)
3rd rowPOINT (-122.31676 47.68156)
4th rowPOINT (-122.65223 47.57192)
5th rowPOINT (-122.9131 47.01359)
ValueCountFrequency (%)
point 97684
33.3%
47.67858 1804
 
0.6%
122.13158 1804
 
0.6%
122.31676 1527
 
0.5%
47.68156 1527
 
0.5%
122.2066 1438
 
0.5%
47.67887 1438
 
0.5%
122.35436 1268
 
0.4%
47.67596 1268
 
0.4%
122.21061 1251
 
0.4%
Other values (1513) 182043
62.1%
2025-06-20T16:28:33.333500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 263262
 
10.1%
1 215007
 
8.2%
4 199217
 
7.6%
. 195368
 
7.5%
195368
 
7.5%
7 172289
 
6.6%
6 110701
 
4.2%
5 110197
 
4.2%
3 106579
 
4.1%
8 104104
 
4.0%
Other values (10) 939753
36.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1439638
55.1%
Uppercase Letter 488420
 
18.7%
Other Punctuation 195368
 
7.5%
Space Separator 195368
 
7.5%
Close Punctuation 97684
 
3.7%
Open Punctuation 97684
 
3.7%
Dash Punctuation 97683
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 263262
18.3%
1 215007
14.9%
4 199217
13.8%
7 172289
12.0%
6 110701
7.7%
5 110197
7.7%
3 106579
7.4%
8 104104
 
7.2%
9 80005
 
5.6%
0 78277
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
P 97684
20.0%
O 97684
20.0%
T 97684
20.0%
N 97684
20.0%
I 97684
20.0%
Other Punctuation
ValueCountFrequency (%)
. 195368
100.0%
Space Separator
ValueCountFrequency (%)
195368
100.0%
Close Punctuation
ValueCountFrequency (%)
) 97684
100.0%
Open Punctuation
ValueCountFrequency (%)
( 97684
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 97683
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2123425
81.3%
Latin 488420
 
18.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2 263262
12.4%
1 215007
10.1%
4 199217
9.4%
. 195368
9.2%
195368
9.2%
7 172289
 
8.1%
6 110701
 
5.2%
5 110197
 
5.2%
3 106579
 
5.0%
8 104104
 
4.9%
Other values (5) 451333
21.3%
Latin
ValueCountFrequency (%)
P 97684
20.0%
O 97684
20.0%
T 97684
20.0%
N 97684
20.0%
I 97684
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2611845
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 263262
 
10.1%
1 215007
 
8.2%
4 199217
 
7.6%
. 195368
 
7.5%
195368
 
7.5%
7 172289
 
6.6%
6 110701
 
4.2%
5 110197
 
4.2%
3 106579
 
4.1%
8 104104
 
4.0%
Other values (10) 939753
36.0%
Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size763.3 KiB
2025-06-20T16:28:33.840829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length112
Median length110
Mean length44.997246
Min length0

Characters and Unicode

Total characters4395781
Distinct characters36
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
2nd rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
3rd rowCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
4th rowPUGET SOUND ENERGY INC
5th rowPUGET SOUND ENERGY INC
ValueCountFrequency (%)
of 90586
12.4%
83379
11.4%
wa 55895
 
7.7%
tacoma 54810
 
7.5%
sound 54057
 
7.4%
energy 54057
 
7.4%
puget 53378
 
7.3%
inc||city 30743
 
4.2%
power 25794
 
3.5%
bonneville 22687
 
3.1%
Other values (111) 205053
28.1%
2025-06-20T16:28:34.845662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
632752
14.4%
O 330490
 
7.5%
N 321314
 
7.3%
T 302434
 
6.9%
A 294658
 
6.7%
E 284640
 
6.5%
I 248808
 
5.7%
C 231472
 
5.3%
Y 153947
 
3.5%
| 149617
 
3.4%
Other values (26) 1445649
32.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3351666
76.2%
Space Separator 632752
 
14.4%
Math Symbol 149617
 
3.4%
Close Punctuation 80079
 
1.8%
Dash Punctuation 80079
 
1.8%
Open Punctuation 80079
 
1.8%
Decimal Number 17611
 
0.4%
Other Punctuation 3898
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 330490
 
9.9%
N 321314
 
9.6%
T 302434
 
9.0%
A 294658
 
8.8%
E 284640
 
8.5%
I 248808
 
7.4%
C 231472
 
6.9%
Y 153947
 
4.6%
U 149520
 
4.5%
R 122891
 
3.7%
Other values (14) 911492
27.2%
Decimal Number
ValueCountFrequency (%)
1 16205
92.0%
2 593
 
3.4%
3 487
 
2.8%
5 326
 
1.9%
Other Punctuation
ValueCountFrequency (%)
& 3300
84.7%
# 326
 
8.4%
, 272
 
7.0%
Space Separator
ValueCountFrequency (%)
632752
100.0%
Math Symbol
ValueCountFrequency (%)
| 149617
100.0%
Close Punctuation
ValueCountFrequency (%)
) 80079
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80079
100.0%
Open Punctuation
ValueCountFrequency (%)
( 80079
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3351666
76.2%
Common 1044115
 
23.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 330490
 
9.9%
N 321314
 
9.6%
T 302434
 
9.0%
A 294658
 
8.8%
E 284640
 
8.5%
I 248808
 
7.4%
C 231472
 
6.9%
Y 153947
 
4.6%
U 149520
 
4.5%
R 122891
 
3.7%
Other values (14) 911492
27.2%
Common
ValueCountFrequency (%)
632752
60.6%
| 149617
 
14.3%
) 80079
 
7.7%
- 80079
 
7.7%
( 80079
 
7.7%
1 16205
 
1.6%
& 3300
 
0.3%
2 593
 
0.1%
3 487
 
< 0.1%
# 326
 
< 0.1%
Other values (2) 598
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4395781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
632752
14.4%
O 330490
 
7.5%
N 321314
 
7.3%
T 302434
 
6.9%
A 294658
 
6.7%
E 284640
 
6.5%
I 248808
 
5.7%
C 231472
 
5.3%
Y 153947
 
3.5%
| 149617
 
3.4%
Other values (26) 1445649
32.9%

census_tract
Real number (ℝ)

Skewed 

Distinct2011
Distinct (%)2.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.2966295 × 1010
Minimum1.0010201 × 109
Maximum6.6010951 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size763.3 KiB
2025-06-20T16:28:35.291839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.0010201 × 109
5-th percentile5.3011041 × 1010
Q15.3033008 × 1010
median5.303303 × 1010
Q35.305394 × 1010
95-th percentile5.3067012 × 1010
Maximum6.6010951 × 1010
Range6.5009931 × 1010
Interquartile range (IQR)20932207

Descriptive statistics

Standard deviation1.7097359 × 109
Coefficient of variation (CV)0.032279696
Kurtosis636.7919
Mean5.2966295 × 1010
Median Absolute Deviation (MAD)2061599
Skewness-24.835819
Sum5.1741185 × 1015
Variance2.9231969 × 1018
MonotonicityNot monotonic
2025-06-20T16:28:35.773427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.30330262 × 1010813
 
0.8%
5.30330285 × 1010446
 
0.5%
5.30330085 × 1010354
 
0.4%
5.303303232 × 1010336
 
0.3%
5.30330078 × 1010308
 
0.3%
5.30330056 × 1010298
 
0.3%
5.30670112 × 1010295
 
0.3%
5.30330245 × 1010287
 
0.3%
5.30330282 × 1010278
 
0.3%
5.30330246 × 1010262
 
0.3%
Other values (2001) 94010
96.2%
ValueCountFrequency (%)
1001020100 1
< 0.1%
1001020804 1
< 0.1%
1081041901 1
< 0.1%
1101000900 1
< 0.1%
1101005503 1
< 0.1%
1121010302 1
< 0.1%
4013103219 1
< 0.1%
4013216901 1
< 0.1%
4013610201 1
< 0.1%
4021000323 1
< 0.1%
ValueCountFrequency (%)
6.60109507 × 10101
 
< 0.1%
5.60210007 × 10101
 
< 0.1%
5.50090205 × 10101
 
< 0.1%
5.307794001 × 10104
< 0.1%
5.307794001 × 10103
< 0.1%
5.307794001 × 10104
< 0.1%
5.307794 × 10106
< 0.1%
5.307794 × 10105
< 0.1%
5.307794 × 10103
< 0.1%
5.307794 × 10106
< 0.1%

legislative_district
Real number (ℝ)

Distinct49
Distinct (%)0.1%
Missing260
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean28.732033
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size763.3 KiB
2025-06-20T16:28:36.251019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q118
median32
Q341
95-th percentile48
Maximum49
Range48
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.527743
Coefficient of variation (CV)0.50562878
Kurtosis-1.090173
Mean28.732033
Median Absolute Deviation (MAD)11
Skewness-0.40680372
Sum2799362
Variance211.05531
MonotonicityNot monotonic
2025-06-20T16:28:36.755499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
41 5081
 
5.2%
45 4842
 
5.0%
48 4179
 
4.3%
36 3942
 
4.0%
46 3902
 
4.0%
43 3584
 
3.7%
1 3440
 
3.5%
5 3367
 
3.4%
34 3166
 
3.2%
11 2925
 
3.0%
Other values (39) 59002
60.4%
ValueCountFrequency (%)
1 3440
3.5%
2 1279
 
1.3%
3 589
 
0.6%
4 1022
 
1.0%
5 3367
3.4%
6 1164
 
1.2%
7 581
 
0.6%
8 1351
 
1.4%
9 705
 
0.7%
10 1989
2.0%
ValueCountFrequency (%)
49 1683
 
1.7%
48 4179
4.3%
47 1569
 
1.6%
46 3902
4.0%
45 4842
5.0%
44 2260
2.3%
43 3584
3.7%
42 1706
 
1.7%
41 5081
5.2%
40 2663
2.7%

Interactions

2025-06-20T16:28:14.183302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:26:50.862457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:27:04.387685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:27:20.145653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:28:01.841346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:28:14.536532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:26:51.260449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:27:04.843342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:27:27.952752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:28:02.209565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:28:15.017851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:26:51.749074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:27:05.439535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:27:36.764073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:28:02.673920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:28:16.899316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:27:03.576849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:27:19.105887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:27:53.529800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:28:13.416724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:28:17.293423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:27:03.999285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:27:19.599990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:28:00.220641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-20T16:28:13.784352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-20T16:28:37.074616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
cafv_eligibilitycensus_tractelectric_rangeev_typelegislative_districtmakemodel_yearpostal_code
cafv_eligibility1.0000.0050.7780.5420.0570.7580.2530.158
census_tract0.0051.000-0.0060.011-0.1540.012-0.0120.059
electric_range0.778-0.0061.0000.9690.0530.526-0.217-0.074
ev_type0.5420.0110.9691.0000.0890.8780.6560.167
legislative_district0.057-0.1540.0530.0891.0000.074-0.035-0.385
make0.7580.0120.5260.8780.0741.0000.3530.094
model_year0.253-0.012-0.2170.656-0.0350.3531.0000.008
postal_code0.1580.059-0.0740.167-0.3850.0940.0081.000

Missing values

2025-06-20T16:28:18.251032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-20T16:28:19.196177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-20T16:28:20.165625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

model_yearmakemodelev_typeelectric_rangecafv_eligibilitycountycitypostal_codevehicle_locationelectric_utilitycensus_tractlegislative_district
02016NISSANLEAFBattery Electric Vehicle (BEV)84Clean Alternative Fuel Vehicle EligibleKingSeattle98125POINT (-122.30253 47.72656)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303300e+1046.0
12016KIASOULBattery Electric Vehicle (BEV)93Clean Alternative Fuel Vehicle EligibleKingRenton98058POINT (-122.08747 47.4466)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+1011.0
22018TESLAMODEL 3Battery Electric Vehicle (BEV)215Clean Alternative Fuel Vehicle EligibleKingSeattle98115POINT (-122.31676 47.68156)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303300e+1043.0
32024JEEPWRANGLERPlug-in Hybrid Electric Vehicle (PHEV)21Not eligible due to low battery rangeKitsapBremerton98312POINT (-122.65223 47.57192)PUGET SOUND ENERGY INC5.303508e+1026.0
42018TESLAMODEL 3Battery Electric Vehicle (BEV)215Clean Alternative Fuel Vehicle EligibleThurstonOlympia98512POINT (-122.9131 47.01359)PUGET SOUND ENERGY INC5.306701e+1035.0
52018TESLAMODEL XBattery Electric Vehicle (BEV)238Clean Alternative Fuel Vehicle EligibleKingSeattle98177POINT (-122.36498 47.72238)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303300e+1032.0
62018TESLAMODEL SBattery Electric Vehicle (BEV)249Clean Alternative Fuel Vehicle EligibleYakimaToppenish98948POINT (-120.31298 46.37508)PACIFICORP5.307794e+1015.0
72015NISSANLEAFBattery Electric Vehicle (BEV)84Clean Alternative Fuel Vehicle EligibleKitsapBainbridge Island98110POINT (-122.521 47.62728)PUGET SOUND ENERGY INC5.303509e+1023.0
82019TESLAMODEL 3Battery Electric Vehicle (BEV)220Clean Alternative Fuel Vehicle EligibleKingSeattle98109POINT (-122.35022 47.63824)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303301e+1036.0
92018TESLAMODEL 3Battery Electric Vehicle (BEV)215Clean Alternative Fuel Vehicle EligibleKingKirkland98034POINT (-122.22901 47.72201)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+101.0
model_yearmakemodelev_typeelectric_rangecafv_eligibilitycountycitypostal_codevehicle_locationelectric_utilitycensus_tractlegislative_district
976802020VOLVOXC90Plug-in Hybrid Electric Vehicle (PHEV)18Not eligible due to low battery rangeKingSeattle98117POINT (-122.38418 47.70044)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303300e+1036.0
976812017NISSANLEAFBattery Electric Vehicle (BEV)107Clean Alternative Fuel Vehicle EligibleWhatcomBlaine98230POINT (-122.74888 48.99404)PUGET SOUND ENERGY INC||PUD NO 1 OF WHATCOM COUNTY5.307301e+1042.0
976822018TESLAMODEL 3Battery Electric Vehicle (BEV)215Clean Alternative Fuel Vehicle EligibleKingSeattle98178POINT (-122.23825 47.49461)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+1037.0
976832016AUDIA3Plug-in Hybrid Electric Vehicle (PHEV)16Not eligible due to low battery rangeMasonShelton98584POINT (-123.10565 47.21248)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PUD NO 3 OF MASON COUNTY5.304596e+1035.0
976842023CHRYSLERPACIFICAPlug-in Hybrid Electric Vehicle (PHEV)32Clean Alternative Fuel Vehicle EligibleSnohomishEdmonds98026POINT (-122.31768 47.87166)PUGET SOUND ENERGY INC5.306105e+1021.0
976852017FIAT500Battery Electric Vehicle (BEV)84Clean Alternative Fuel Vehicle EligibleSpokaneSpokane99218POINT (-117.4118 47.7458)BONNEVILLE POWER ADMINISTRATION||AVISTA CORP||INLAND POWER & LIGHT COMPANY5.306301e+106.0
976862013CHEVROLETVOLTPlug-in Hybrid Electric Vehicle (PHEV)38Clean Alternative Fuel Vehicle EligibleSnohomishEverett98208POINT (-122.18637 47.89251)PUGET SOUND ENERGY INC5.306104e+1044.0
976872024JEEPGRAND CHEROKEEPlug-in Hybrid Electric Vehicle (PHEV)25Not eligible due to low battery rangePiercePuyallup98374POINT (-122.27575 47.13959)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.305307e+1025.0
976882023KIASPORTAGEPlug-in Hybrid Electric Vehicle (PHEV)34Clean Alternative Fuel Vehicle EligibleKitsapBainbridge Island98110POINT (-122.521 47.62728)PUGET SOUND ENERGY INC5.303509e+1023.0
976892023KIASPORTAGEPlug-in Hybrid Electric Vehicle (PHEV)34Clean Alternative Fuel Vehicle EligibleWhatcomBellingham98226POINT (-122.49756 48.7999)PUGET SOUND ENERGY INC||PUD NO 1 OF WHATCOM COUNTY5.307300e+1042.0

Duplicate rows

Most frequently occurring

model_yearmakemodelev_typeelectric_rangecafv_eligibilitycountycitypostal_codevehicle_locationelectric_utilitycensus_tractlegislative_district# duplicates
136182025JEEPWRANGLERPlug-in Hybrid Electric Vehicle (PHEV)21Not eligible due to low battery rangeKingSeatac98148POINT (-122.32863 47.46233)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+1033.0380
121772024CHRYSLERPACIFICAPlug-in Hybrid Electric Vehicle (PHEV)32Clean Alternative Fuel Vehicle EligibleKingRenton98057POINT (-122.20489 47.47532)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+1011.0260
113682023CHRYSLERPACIFICAPlug-in Hybrid Electric Vehicle (PHEV)32Clean Alternative Fuel Vehicle EligibleKingRenton98057POINT (-122.20489 47.47532)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+1011.0159
136192025JEEPWRANGLERPlug-in Hybrid Electric Vehicle (PHEV)21Not eligible due to low battery rangeKingTukwila98188POINT (-122.29179 47.43473)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+1011.0149
122852024JEEPGRAND CHEROKEEPlug-in Hybrid Electric Vehicle (PHEV)25Not eligible due to low battery rangeKingRenton98057POINT (-122.20489 47.47532)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+1011.0137
124412024JEEPWRANGLERPlug-in Hybrid Electric Vehicle (PHEV)21Not eligible due to low battery rangeKingRenton98057POINT (-122.20489 47.47532)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+1011.0110
29652017CHEVROLETBOLT EVBattery Electric Vehicle (BEV)238Clean Alternative Fuel Vehicle EligibleThurstonOlympia98501POINT (-122.89165 47.03954)PUGET SOUND ENERGY INC5.306701e+1022.0100
136162025JEEPWRANGLERPlug-in Hybrid Electric Vehicle (PHEV)21Not eligible due to low battery rangeClarkVancouver98682POINT (-122.55149 45.69345)BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)5.301104e+1017.099
121822024CHRYSLERPACIFICAPlug-in Hybrid Electric Vehicle (PHEV)32Clean Alternative Fuel Vehicle EligibleSpokaneSpokane Valley99212POINT (-117.28805 47.68043)BONNEVILLE POWER ADMINISTRATION||VERA IRRIGATION DISTRICT #155.306301e+104.056
121802024CHRYSLERPACIFICAPlug-in Hybrid Electric Vehicle (PHEV)32Clean Alternative Fuel Vehicle EligibleLewisCentralia98531POINT (-122.95266 46.72895)BONNEVILLE POWER ADMINISTRATION||CITY OF CENTRALIA - (WA)|CITY OF TACOMA - (WA)5.304197e+1020.051